Software debugging is tedious, time-consuming, and even errorprone by itself. So, various automated debugging techniques have been proposed in the literature to facilitate the debugging process. Automated Program Repair (APR) is one of the most recent advances in automated debugging, and can directly produce patches for buggy programs with minimal human intervention. Although various advanced APR techniques (including those that are either search-based or semantic-based) have been proposed, the simplistic mutation-based APR technique, which simply uses pre-defined mutation operators (e.g., changing a>=b into a>b) to mutate programs for finding patches, has not yet been thoroughly studied. In this paper, we implement the first practical bytecode-level APR technique, PraPR, and present the first extensive study on fixing real-world bugs (e.g., Defects4J bugs) using bytecode mutation. The experimental results show that surprisingly even PraPR with only the basic traditional mutators can produce genuine patches for 18 bugs. Furthermore, with our augmented mutators, PraPR is able to produce genuine patches for 43 bugs, significantly outperforming state-of-the-art APR. It is also an order of magnitude faster, indicating a promising future for bytecode-mutation-based APR.
Background: Food insecurity as a major public health problem has associations with a wide range of adverse consequences on health and quality of life. The aim of this study is to determine the prevalence of food insecurity among Iranian households, its key socioeconomic risk factors and population attributable risk via a large-scale cross-sectional study in the capital of Iran. Methods: This cross-sectional study was performed among 30,809 households with complete questionnaires of food security, during 2011. The univariate test was used to investigate the association between economic status and covariates with household food insecurity. Multiple logistic regression model was used to assess the independent effect of economic status on household food insecurity. Results: Totally, 37.8% (95% CI: 37.25, 38.34%) of the households were food insecure. There were significant associations between economic status and household food insecurity after adjustment for other variables (p-value<0.001). The extent of household food insecurity that could be attributed to the economic status in the 1st and 2nd quintiles (poorest and poor households), compared with the 5th quintile (richest households), was estimated to be 48.43% and 60.12%, respectively. Conclusion: Food insecurity is relatively prevalent among households in Tehran. Economic status was identified as the most significant determinant of household food security, as 62.7% of poorest households were food insecure. Therefore, there is a crucial need to address food insecurity as a priority in food policies.
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